





'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)






def   __A    (				a_    :	Dict   ,a_    :	Any=False						):
		lowerCAmelCase  :     str       						= OrderedDict()
		for key, value in state_dict.items():
				if encoder_only and not key.startswith("head"						):
						lowerCAmelCase  :     str       						= "segformer.encoder." + key
				if key.startswith("backbone"						):
						lowerCAmelCase  :     Any       						= key.replace("backbone"   ,"segformer.encoder"						)
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowerCAmelCase  :     Dict       						= key[key.find("patch_embed"						) + len("patch_embed"						)]
						lowerCAmelCase  :     Tuple       						= key.replace(f'''patch_embed{idx}'''   ,f'''patch_embeddings.{int(a_						)-1}'''						)
				if "norm" in key:
						lowerCAmelCase  :     str       						= key.replace("norm"   ,"layer_norm"						)
				if "segformer.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowerCAmelCase  :     Tuple       						= key[key.find("segformer.encoder.layer_norm"						) + len("segformer.encoder.layer_norm"						)]
						lowerCAmelCase  :     List[str]       						= key.replace(f'''layer_norm{idx}'''   ,f'''layer_norm.{int(a_						)-1}'''						)
				if "layer_norm1" in key:
						lowerCAmelCase  :     Union[str, Any]       						= key.replace("layer_norm1"   ,"layer_norm_1"						)
				if "layer_norm2" in key:
						lowerCAmelCase  :     Tuple       						= key.replace("layer_norm2"   ,"layer_norm_2"						)
				if "block" in key:
						# replace for example block1 by block.0
						lowerCAmelCase  :     Tuple       						= key[key.find("block"						) + len("block"						)]
						lowerCAmelCase  :     str       						= key.replace(f'''block{idx}'''   ,f'''block.{int(a_						)-1}'''						)
				if "attn.q" in key:
						lowerCAmelCase  :     Tuple       						= key.replace("attn.q"   ,"attention.self.query"						)
				if "attn.proj" in key:
						lowerCAmelCase  :     str       						= key.replace("attn.proj"   ,"attention.output.dense"						)
				if "attn" in key:
						lowerCAmelCase  :     Any       						= key.replace("attn"   ,"attention.self"						)
				if "fc1" in key:
						lowerCAmelCase  :     Any       						= key.replace("fc1"   ,"dense1"						)
				if "fc2" in key:
						lowerCAmelCase  :     int       						= key.replace("fc2"   ,"dense2"						)
				if "linear_pred" in key:
						lowerCAmelCase  :     str       						= key.replace("linear_pred"   ,"classifier"						)
				if "linear_fuse" in key:
						lowerCAmelCase  :     List[str]       						= key.replace("linear_fuse.conv"   ,"linear_fuse"						)
						lowerCAmelCase  :     Dict       						= key.replace("linear_fuse.bn"   ,"batch_norm"						)
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowerCAmelCase  :     str       						= key[key.find("linear_c"						) + len("linear_c"						)]
						lowerCAmelCase  :     List[Any]       						= key.replace(f'''linear_c{idx}'''   ,f'''linear_c.{int(a_						)-1}'''						)
				if key.startswith("head"						):
						lowerCAmelCase  :     int       						= key.replace("head"   ,"classifier"						)
				lowerCAmelCase  :     Union[str, Any]       						= value

		return new_state_dict






def   __A    (				a_    :	Tuple   ,a_    :	str						):
		# for each of the encoder blocks:
		for i in range(config.num_encoder_blocks						):
				for j in range(config.depths[i]						):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowerCAmelCase  :     Any       						= state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''						)
						lowerCAmelCase  :     Optional[Any]       						= state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''						)
						# next, add keys and values (in that order) to the state dict
						lowerCAmelCase  :     Union[str, Any]       						= kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCAmelCase  :     Dict       						= kv_bias[: config.hidden_sizes[i]]
						lowerCAmelCase  :     Dict       						= kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCAmelCase  :     Dict       						= kv_bias[
						    config.hidden_sizes[i] :
						]






def   __A    (				):
		lowerCAmelCase  :     int       						= "http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase  :     Union[str, Any]       						= Image.open(requests.get(a_   ,stream=a_						).raw						)

		return image






@torch.no_grad()
def   __A    (				a_    :	List[str]   ,a_    :	List[str]   ,a_    :	Dict						):
		lowerCAmelCase  :     str       						= SegformerConfig()
		lowerCAmelCase  :     List[Any]       						= False

		# set attributes based on model_name
		lowerCAmelCase  :     List[Any]       						= "huggingface/label-files"
		if "segformer" in model_name:
				lowerCAmelCase  :     List[str]       						= model_name[len("segformer."						) : len("segformer."						) + 2]
				if "ade" in model_name:
						lowerCAmelCase  :     Union[str, Any]       						= 1_5_0
						lowerCAmelCase  :     List[str]       						= "ade20k-id2label.json"
						lowerCAmelCase  :     Optional[int]       						= (1, 1_5_0, 1_2_8, 1_2_8)
				elif "city" in model_name:
						lowerCAmelCase  :     Optional[int]       						= 1_9
						lowerCAmelCase  :     int       						= "cityscapes-id2label.json"
						lowerCAmelCase  :     str       						= (1, 1_9, 1_2_8, 1_2_8)
				else:
						raise ValueError(f'''Model {model_name} not supported'''						)
		elif "mit" in model_name:
				lowerCAmelCase  :     str       						= True
				lowerCAmelCase  :     List[str]       						= model_name[4:6]
				lowerCAmelCase  :     Optional[int]       						= 1_0_0_0
				lowerCAmelCase  :     List[Any]       						= "imagenet-1k-id2label.json"
				lowerCAmelCase  :     Optional[int]       						= (1, 1_0_0_0)
		else:
				raise ValueError(f'''Model {model_name} not supported'''						)

		# set config attributes
		lowerCAmelCase  :     Optional[int]       						= json.load(open(hf_hub_download(a_   ,a_   ,repo_type="dataset"						)   ,"r"						)						)
		lowerCAmelCase  :     Union[str, Any]       						= {int(a_						): v for k, v in idalabel.items()}
		lowerCAmelCase  :     Tuple       						= idalabel
		lowerCAmelCase  :     Optional[int]       						= {v: k for k, v in idalabel.items()}
		if size == "b0":
				pass
		elif size == "b1":
				lowerCAmelCase  :     Dict       						= [6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCAmelCase  :     Dict       						= 2_5_6
		elif size == "b2":
				lowerCAmelCase  :     Any       						= [6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCAmelCase  :     Optional[int]       						= 7_6_8
				lowerCAmelCase  :     Union[str, Any]       						= [3, 4, 6, 3]
		elif size == "b3":
				lowerCAmelCase  :     Union[str, Any]       						= [6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCAmelCase  :     Dict       						= 7_6_8
				lowerCAmelCase  :     List[Any]       						= [3, 4, 1_8, 3]
		elif size == "b4":
				lowerCAmelCase  :     Any       						= [6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCAmelCase  :     int       						= 7_6_8
				lowerCAmelCase  :     List[str]       						= [3, 8, 2_7, 3]
		elif size == "b5":
				lowerCAmelCase  :     Optional[int]       						= [6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCAmelCase  :     Optional[Any]       						= 7_6_8
				lowerCAmelCase  :     Tuple       						= [3, 6, 4_0, 3]
		else:
				raise ValueError(f'''Size {size} not supported'''						)

		# load image processor (only resize + normalize)
		lowerCAmelCase  :     Tuple       						= SegformerImageProcessor(
		    image_scale=(5_1_2, 5_1_2)   ,keep_ratio=a_   ,align=a_   ,do_random_crop=a_						)

		# prepare image
		lowerCAmelCase  :     Tuple       						= prepare_img()
		lowerCAmelCase  :     int       						= image_processor(images=a_   ,return_tensors="pt"						).pixel_values

		logger.info(f'''Converting model {model_name}...'''						)

		# load original state dict
		if encoder_only:
				lowerCAmelCase  :     Dict       						= torch.load(a_   ,map_location=torch.device("cpu"						)						)
		else:
				lowerCAmelCase  :     Union[str, Any]       						= torch.load(a_   ,map_location=torch.device("cpu"						)						)["state_dict"]

		# rename keys
		lowerCAmelCase  :     Any       						= rename_keys(a_   ,encoder_only=a_						)
		if not encoder_only:
				del state_dict["decode_head.conv_seg.weight"]
				del state_dict["decode_head.conv_seg.bias"]

		# key and value matrices need special treatment
		read_in_k_v(a_   ,a_						)

		# create HuggingFace model and load state dict
		if encoder_only:
				lowerCAmelCase  :     List[str]       						= False
				lowerCAmelCase  :     Union[str, Any]       						= SegformerForImageClassification(a_						)
		else:
				lowerCAmelCase  :     Optional[int]       						= SegformerForSemanticSegmentation(a_						)
		model.load_state_dict(a_						)
		model.eval()

		# forward pass
		lowerCAmelCase  :     Optional[int]       						= model(a_						)
		lowerCAmelCase  :     List[str]       						= outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				lowerCAmelCase  :     List[Any]       						= torch.tensor(
				    [
				        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
				        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
				        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
				    ]						)
		elif model_name == "segformer.b1.512x512.ade.160k":
				lowerCAmelCase  :     List[str]       						= torch.tensor(
				    [
				        [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
				        [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
				        [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
				    ]						)
		elif model_name == "segformer.b2.512x512.ade.160k":
				lowerCAmelCase  :     Optional[int]       						= torch.tensor(
				    [
				        [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
				        [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
				        [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
				    ]						)
		elif model_name == "segformer.b3.512x512.ade.160k":
				lowerCAmelCase  :     Optional[Any]       						= torch.tensor(
				    [
				        [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
				        [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
				        [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
				    ]						)
		elif model_name == "segformer.b4.512x512.ade.160k":
				lowerCAmelCase  :     str       						= torch.tensor(
				    [
				        [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
				        [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
				        [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
				    ]						)
		elif model_name == "segformer.b5.640x640.ade.160k":
				lowerCAmelCase  :     int       						= torch.tensor(
				    [
				        [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
				        [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
				        [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
				    ]						)
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				lowerCAmelCase  :     Union[str, Any]       						= torch.tensor(
				    [
				        [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
				        [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
				        [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
				    ]						)
		elif model_name == "segformer.b0.512x1024.city.160k":
				lowerCAmelCase  :     Optional[Any]       						= torch.tensor(
				    [
				        [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
				        [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
				        [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
				    ]						)
		elif model_name == "segformer.b0.640x1280.city.160k":
				lowerCAmelCase  :     Any       						= torch.tensor(
				    [
				        [
				            [-1.1372e01, -1.2787e01, -1.3477e01],
				            [-1.2536e01, -1.4194e01, -1.4409e01],
				            [-1.3217e01, -1.4888e01, -1.5327e01],
				        ],
				        [
				            [-1.4791e01, -1.7122e01, -1.8277e01],
				            [-1.7163e01, -1.9192e01, -1.9533e01],
				            [-1.7897e01, -1.9991e01, -2.0315e01],
				        ],
				        [
				            [7.6723e-01, 4.1921e-01, -7.7878e-02],
				            [4.7772e-01, 9.5557e-03, -2.8082e-01],
				            [3.6032e-01, -2.4826e-01, -5.1168e-01],
				        ],
				    ]						)
		elif model_name == "segformer.b0.768x768.city.160k":
				lowerCAmelCase  :     List[str]       						= torch.tensor(
				    [
				        [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
				        [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
				        [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
				    ]						)
		elif model_name == "segformer.b1.1024x1024.city.160k":
				lowerCAmelCase  :     Dict       						= torch.tensor(
				    [
				        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
				        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
				        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
				    ]						)
		elif model_name == "segformer.b2.1024x1024.city.160k":
				lowerCAmelCase  :     List[Any]       						= torch.tensor(
				    [
				        [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
				        [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
				        [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
				    ]						)
		elif model_name == "segformer.b3.1024x1024.city.160k":
				lowerCAmelCase  :     int       						= torch.tensor(
				    [
				        [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
				        [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
				        [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
				    ]						)
		elif model_name == "segformer.b4.1024x1024.city.160k":
				lowerCAmelCase  :     Any       						= torch.tensor(
				    [
				        [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
				        [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
				        [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
				    ]						)
		elif model_name == "segformer.b5.1024x1024.city.160k":
				lowerCAmelCase  :     Dict       						= torch.tensor(
				    [
				        [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
				        [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
				        [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
				    ]						)
		else:
				lowerCAmelCase  :     Union[str, Any]       						= logits.argmax(-1						).item()
				print("Predicted class:"   ,model.config.idalabel[predicted_class_idx]						)

		# verify logits
		if not encoder_only:
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3, :3, :3]   ,a_   ,atol=1e-2						)

		# finally, save model and image processor
		logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''						)
		Path(a_						).mkdir(exist_ok=a_						)
		model.save_pretrained(a_						)
		image_processor.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    default="""segformer.b0.512x512.ade.160k""",
					    type=str,
					    help="""Name of the model you'd like to convert.""",
					)
					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def   __A    (				a_    :	int   ,a_    :	str=0.9_9_9   ,a_    :	List[str]="cosine"   ,):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(a_    :	List[Any]						):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(a_    :	List[str]						):
						return math.exp(t * -1_2.0						)

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

		lowerCAmelCase  :     Optional[int]       						= []
		for i in range(a_						):
				lowerCAmelCase  :     Dict       						= i / num_diffusion_timesteps
				lowerCAmelCase  :     List[Any]       						= (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(a_						) / alpha_bar_fn(a_						)   ,a_						)						)
		return torch.tensor(a_   ,dtype=torch.floataa						)

class 		lowerCamelCase	(   _A   , _A							):
	snake_case_   = [e.name for e in KarrasDiffusionSchedulers]
	snake_case_   = 2



	@register_to_config
	def __init__( self		,   a_ = 1_000		,   a_ = 0.00085		,   a_ = 0.012		,   a_ = "linear"		,   a_ = None		,   a_ = "epsilon"		,   a_ = False		,   a_ = False		,   a_ = 1.0		,   a_ = "linspace"		,   a_ = 0		,   ):
			if trained_betas is not None:
					lowerCAmelCase  :     List[Any]       						= torch.tensor(a_		,   dtype=torch.floataa						)
			elif beta_schedule == "linear":
					lowerCAmelCase  :     List[str]       						= torch.linspace(a_		,   a_		,   a_		,   dtype=torch.floataa						)
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					lowerCAmelCase  :     List[str]       						= (
					    torch.linspace(beta_start**0.5		,   beta_end**0.5		,   a_		,   dtype=torch.floataa						) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					lowerCAmelCase  :     List[Any]       						= betas_for_alpha_bar(a_		,   alpha_transform_type="cosine"						)
			elif beta_schedule == "exp":
					lowerCAmelCase  :     Any       						= betas_for_alpha_bar(a_		,   alpha_transform_type="exp"						)
			else:
					raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

			lowerCAmelCase  :     Optional[int]       						= 1.0 - self.betas
			lowerCAmelCase  :     Optional[Any]       						= torch.cumprod(self.alphas		,   dim=0						)

			#  set all values
			self.set_timesteps(a_		,   a_		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= use_karras_sigmas



	def 	_lowerCamelCase     ( self		,   a_		,   a_=None						):
			if schedule_timesteps is None:
					lowerCAmelCase  :     List[str]       						= self.timesteps

			lowerCAmelCase  :     int       						= (schedule_timesteps == timestep).nonzero()

			# The sigma index that is taken for the **very** first `step`
			# is always the second index (or the last index if there is only 1)
			# This way we can ensure we don't accidentally skip a sigma in
			# case we start in the middle of the denoising schedule (e.g. for image-to-image)
			if len(self._index_counter						) == 0:
					lowerCAmelCase  :     Union[str, Any]       						= 1 if len(a_						) > 1 else 0
			else:
					lowerCAmelCase  :     Tuple       						= timestep.cpu().item() if torch.is_tensor(a_						) else timestep
					lowerCAmelCase  :     Any       						= self._index_counter[timestep_int]

			return indices[pos].item()



	@property
	def 	_lowerCamelCase     ( self						):
			# standard deviation of the initial noise distribution
			if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

			return (self.sigmas.max() ** 2 + 1) ** 0.5



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   ):
			lowerCAmelCase  :     Union[str, Any]       						= self.index_for_timestep(a_						)

			lowerCAmelCase  :     Any       						= self.sigmas[step_index]
			lowerCAmelCase  :     int       						= sample / ((sigma**2 + 1) ** 0.5)
			return sample



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   ):
			lowerCAmelCase  :     List[Any]       						= num_inference_steps

			lowerCAmelCase  :     Optional[int]       						= num_train_timesteps or self.config.num_train_timesteps

			# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
			if self.config.timestep_spacing == "linspace":
					lowerCAmelCase  :     Optional[Any]       						= np.linspace(0		,   num_train_timesteps - 1		,   a_		,   dtype=a_						)[::-1].copy()
			elif self.config.timestep_spacing == "leading":
					lowerCAmelCase  :     str       						= num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					lowerCAmelCase  :     Dict       						= (np.arange(0		,   a_						) * step_ratio).round()[::-1].copy().astype(a_						)
					timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
					lowerCAmelCase  :     Tuple       						= num_train_timesteps / self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					lowerCAmelCase  :     int       						= (np.arange(a_		,   0		,   -step_ratio						)).round().copy().astype(a_						)
					timesteps -= 1
			else:
					raise ValueError(
					    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

			lowerCAmelCase  :     Any       						= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
			lowerCAmelCase  :     Any       						= np.log(a_						)
			lowerCAmelCase  :     int       						= np.interp(a_		,   np.arange(0		,   len(a_						)						)		,   a_						)

			if self.config.use_karras_sigmas:
					lowerCAmelCase  :     int       						= self._convert_to_karras(in_sigmas=a_		,   num_inference_steps=self.num_inference_steps						)
					lowerCAmelCase  :     Any       						= np.array([self._sigma_to_t(a_		,   a_						) for sigma in sigmas]						)

			lowerCAmelCase  :     int       						= np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
			lowerCAmelCase  :     int       						= torch.from_numpy(a_						).to(device=a_						)
			lowerCAmelCase  :     Any       						= torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2						), sigmas[-1:]]						)

			lowerCAmelCase  :     Union[str, Any]       						= torch.from_numpy(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2						)]						)

			if str(a_						).startswith("mps"						):
					# mps does not support float64
					lowerCAmelCase  :     str       						= timesteps.to(a_		,   dtype=torch.floataa						)
			else:
					lowerCAmelCase  :     int       						= timesteps.to(device=a_						)

			# empty dt and derivative
			lowerCAmelCase  :     Union[str, Any]       						= None
			lowerCAmelCase  :     Optional[Any]       						= None

			# for exp beta schedules, such as the one for `pipeline_shap_e.py`
			# we need an index counter
			lowerCAmelCase  :     int       						= defaultdict(a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# get log sigma
			lowerCAmelCase  :     str       						= np.log(a_						)

			# get distribution
			lowerCAmelCase  :     int       						= log_sigma - log_sigmas[:, np.newaxis]

			# get sigmas range
			lowerCAmelCase  :     int       						= np.cumsum((dists >= 0)		,   axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
			lowerCAmelCase  :     Any       						= low_idx + 1

			lowerCAmelCase  :     str       						= log_sigmas[low_idx]
			lowerCAmelCase  :     Optional[Any]       						= log_sigmas[high_idx]

			# interpolate sigmas
			lowerCAmelCase  :     Optional[Any]       						= (low - log_sigma) / (low - high)
			lowerCAmelCase  :     List[str]       						= np.clip(a_		,   0		,   1						)

			# transform interpolation to time range
			lowerCAmelCase  :     int       						= (1 - w) * low_idx + w * high_idx
			lowerCAmelCase  :     Dict       						= t.reshape(sigma.shape						)
			return t



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			lowerCAmelCase  :     float       						= in_sigmas[-1].item()
			lowerCAmelCase  :     float       						= in_sigmas[0].item()

			lowerCAmelCase  :     str       						= 7.0  # 7.0 is the value used in the paper
			lowerCAmelCase  :     Optional[int]       						= np.linspace(0		,   1		,   a_						)
			lowerCAmelCase  :     Optional[int]       						= sigma_min ** (1 / rho)
			lowerCAmelCase  :     str       						= sigma_max ** (1 / rho)
			lowerCAmelCase  :     List[str]       						= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
			return sigmas



	@property
	def 	_lowerCamelCase     ( self						):
			return self.dt is None



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = True		,   ):
			lowerCAmelCase  :     Optional[int]       						= self.index_for_timestep(a_						)

			# advance index counter by 1
			lowerCAmelCase  :     List[str]       						= timestep.cpu().item() if torch.is_tensor(a_						) else timestep
			self._index_counter[timestep_int] += 1

			if self.state_in_first_order:
					lowerCAmelCase  :     Optional[Any]       						= self.sigmas[step_index]
					lowerCAmelCase  :     Dict       						= self.sigmas[step_index + 1]
			else:
					# 2nd order / Heun's method
					lowerCAmelCase  :     List[str]       						= self.sigmas[step_index - 1]
					lowerCAmelCase  :     int       						= self.sigmas[step_index]

			# currently only gamma=0 is supported. This usually works best anyways.
			# We can support gamma in the future but then need to scale the timestep before
			# passing it to the model which requires a change in API
			lowerCAmelCase  :     int       						= 0
			lowerCAmelCase  :     Optional[int]       						= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			if self.config.prediction_type == "epsilon":
					lowerCAmelCase  :     int       						= sigma_hat if self.state_in_first_order else sigma_next
					lowerCAmelCase  :     Tuple       						= sample - sigma_input * model_output
			elif self.config.prediction_type == "v_prediction":
					lowerCAmelCase  :     List[str]       						= sigma_hat if self.state_in_first_order else sigma_next
					lowerCAmelCase  :     int       						= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
			elif self.config.prediction_type == "sample":
					lowerCAmelCase  :     Optional[int]       						= model_output
			else:
					raise ValueError(
					    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

			if self.config.clip_sample:
					lowerCAmelCase  :     Optional[int]       						= pred_original_sample.clamp(
					    -self.config.clip_sample_range		,   self.config.clip_sample_range						)

			if self.state_in_first_order:
					# 2. Convert to an ODE derivative for 1st order
					lowerCAmelCase  :     Union[str, Any]       						= (sample - pred_original_sample) / sigma_hat
					# 3. delta timestep
					lowerCAmelCase  :     Optional[int]       						= sigma_next - sigma_hat

					# store for 2nd order step
					lowerCAmelCase  :     List[str]       						= derivative
					lowerCAmelCase  :     Optional[int]       						= dt
					lowerCAmelCase  :     Dict       						= sample
			else:
					# 2. 2nd order / Heun's method
					lowerCAmelCase  :     Dict       						= (sample - pred_original_sample) / sigma_next
					lowerCAmelCase  :     Optional[int]       						= (self.prev_derivative + derivative) / 2

					# 3. take prev timestep & sample
					lowerCAmelCase  :     Optional[int]       						= self.dt
					lowerCAmelCase  :     Tuple       						= self.sample

					# free dt and derivative
					# Note, this puts the scheduler in "first order mode"
					lowerCAmelCase  :     Tuple       						= None
					lowerCAmelCase  :     Optional[Any]       						= None
					lowerCAmelCase  :     Union[str, Any]       						= None

			lowerCAmelCase  :     Any       						= sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return SchedulerOutput(prev_sample=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   ):
			# Make sure sigmas and timesteps have the same device and dtype as original_samples
			lowerCAmelCase  :     Tuple       						= self.sigmas.to(device=original_samples.device		,   dtype=original_samples.dtype						)
			if original_samples.device.type == "mps" and torch.is_floating_point(a_						):
					# mps does not support float64
					lowerCAmelCase  :     Optional[int]       						= self.timesteps.to(original_samples.device		,   dtype=torch.floataa						)
					lowerCAmelCase  :     Dict       						= timesteps.to(original_samples.device		,   dtype=torch.floataa						)
			else:
					lowerCAmelCase  :     List[Any]       						= self.timesteps.to(original_samples.device						)
					lowerCAmelCase  :     str       						= timesteps.to(original_samples.device						)

			lowerCAmelCase  :     Tuple       						= [self.index_for_timestep(a_		,   a_						) for t in timesteps]

			lowerCAmelCase  :     Dict       						= sigmas[step_indices].flatten()
			while len(sigma.shape						) < len(original_samples.shape						):
					lowerCAmelCase  :     Any       						= sigma.unsqueeze(-1						)

			lowerCAmelCase  :     str       						= original_samples + noise * sigma
			return noisy_samples





	def __len__( self						):
			return self.config.num_train_timesteps



